Abstract: In order to meet the needs of modern society for weather forecast and early warning service, a real-time weather forecast and abnormal weather early warning system was researched and implemented in the Android system. Based on the decision tree algorithm of C4.5 algorithm, the warning classification problem was resolved. By means of extracting the attributes with maximum gain rate as the features of training sample, a decision tree was built. A model of decision tree was got by the pruning weather warning evaluation and analysis and application were made on this model. The experimental results show that this method has advantages in the assessment of classification accuracy, with correct classification rate up to 85.8%.

Key words: Web Service, weather forecast, decision tree, C4.5 algorithm, pruning, alert

摘要： 为满足现代社会对气象预警预报服务的需求，研发了Android系统平台下实时天气预测和异常天气预警系统。根据决策树算法中的C4.5算法，解决天气预警分类问题。该方法通过提取训练样本中最大增益率属性作为属性特征建立决策树，经剪枝后得到天气预警评估的决策树模型，并对此模型进行分析和应用。实验结果表明这种方法在分类评估准确率上具有优势，分类正确率达到85.8%.

关键词: Web Service, 天气预报, 决策树, C4.5算法, 剪枝, 警报